Genetic Diversity work

Load libraries and file paths

library("plyr")
library("dplyr")
library("ggplot2")
library(R.utils)
library(gghighlight)
library(ggman)
library(ggtext)
library(patchwork)
library(plotrix)
library(qqman)
library(qvalue)
library(reshape2)
library(tidyr)
library(zoo)
library(infer)
options(dplyr.summarise.inform = FALSE)
library(bigsnpr)
library("wesanderson")
library("directlabels")
library(OutFLANK)
library(adegenet)
library(poppr)
library(vcfR)
library(stringr)
library(matrixStats)
library(purrr)
library(scales) 

Nucleotide diversity

Nucleotide diversity (often referred to using the symbol π) is the average pairwise difference between all possible pairs of individuals in your sample. It is a very intuitive and simple measure of genetic diversity, and is accurately estimated even with very few samples. A formal definition is here.

We can obtain the nucleotide diversity (π) from our VCF file using vcftools software. In our case we will collect the π value from each 10 kb (10,000 bp) window of the genome.

NB: vcftools is a very flexible tool for analyzing, manipulating VCF files. It can do many other wonderful things. The vcftools manual is on github here (https://vcftools.sourceforge.net/man_latest.html).

Breaking up pi by treatment type?

I believe that an important step would be to compare nucleotide diversity between the different treatment groups. The following code present information for all treatment groups and compares it to each individual treatment group.

Start of modified workflow

Setup

The following was run on the command line

# Make ROD_CADO_working directory in home
mkdir ROD_CADO_working
cd ROD_CADO_working
# Make Nucleotide_diversity directory
mkdir ROD_CADO_working
# create popmap file with sample and treatment names
cp /home/Shared_Data/ROD_CADO/analysis/popmap popmap
# manually add in treatment names to popmap file (w/ code)
head treat_popmap 

C1_2 con-con C1_4 con-con C1_5 con-con C1_6 con-con C1_7 con-con C1_8 con-con C1_9 con-con C2_10 con-con C2_11 con-con C2_2 con-con

Subsetting popmap groups

Create treatment specific files containing a single column of all of the sample names within that treatment

awk '$2 == "con-con" {print $1}' treat_popmap > con-con.txt | awk '$2 == "str-con" {print $1}' treat_popmap > str-con.txt | awk '$2 == "con-rod" {print $1}' treat_popmap > con-rod.txt | awk '$2 == "str-rod" {print $1}' treat_popmap > str-rod.txt

Run VCF tools PI window

#bcftools view --threads 20 -S SNP.TRSdp10g1.FIL.vcf | vcftools --vcf -  --window-pi 10000 --out ROD.CADO.all.pi

# For con-con
# Step 1: Filter VCF for population subset
vcftools --gzvcf SNP.TRSdp10g1.FIL.vcf.gz --keep con-con.txt --recode --recode-INFO-all --out temp_concon_filtered

# Step 2: bgzip output
bgzip temp_concon_filtered.recode.vcf

# Step 3: Calculate windowed pi
vcftools --gzvcf temp_concon.filtered.recode.vcf.gz --window-pi 10000 --out ROD.CADO.con-con.pi.windowed.pi

# For str-con
# Step 1: Filter VCF for population subset
vcftools --gzvcf SNP.TRSdp10g1.FIL.vcf.gz --keep popmap_files/str-con.txt --recode --recode-INFO-all --out temp_strcon_filtered

# Step 2: bgzip output
bgzip temp_strcon_filtered.recode.vcf

# Step 3: # Step 3: Calculate windowed pi
vcftools --gzvcf temp_strcon_filtered.recode.vcf.gz --window-pi 10000 --out ROD.CADO.str-con.pi.windowed.pi

# For con-rod
# Step 1: Filter VCF for population subset
vcftools --gzvcf SNP.TRSdp10g1.FIL.vcf --keep popmap_files/con-rod.txt --recode --recode-INFO-all --out temp_conrod_filtered

# Step 2: bgzip output
bgzip temp_conrod_filtered.recode.vcf

# Step 3: Calculate windowed pi
vcftools --gzvcf temp_conrod_filtered.recode.vcf.gz --window-pi 10000 --out ROD.CADO.con-rod.pi.windowed.pi

# For str-rod
# Step 1: Filter VCF for population subset
vcftools --gzvcf SNP.TRSdp10g1.FIL.vcf --keep popmap_files/str-rod.txt --recode --recode-INFO-all --out temp_strrod_filtered

# Step 2: bgzip output
bgzip temp_strrod_filtered.recode.vcf

# Step 3: Calculate windowed pi
vcftools --gzvcf temp_strrod_filtered.recode.vcf.gz --window-pi 10000 --out ROD.CADO.str-rod.pi.windowed.pi

Make loop with help from ChatGPT

Make script

#!/bin/bash

VCF=SNP.TRSdp10g1.FIL.vcf.gz
POPS=("con-con" "str-con" "con-rod" "str-rod")

for POP in "${POPS[@]}"; do
    echo "Processing $POP..."

    KEEP="popmap_files/${POP}.txt"
    OUT_PREFIX="temp_${POP//-}"
    REC_VCF="${OUT_PREFIX}.recode.vcf"
    REC_VCFGZ="${REC_VCF}.gz"
    OUTPUT_PI="ROD.CADO.${POP}.pi.windowed.pi"

    # Step 1: Filter and recode
    vcftools --gzvcf "$VCF" \
        --keep "$KEEP" \
        --recode --recode-INFO-all \
        --out "$OUT_PREFIX"

    # Step 2: Compress VCF and remove uncompressed
    bgzip "$REC_VCF"
    rm "$REC_VCF"

    # Step 3: Calculate windowed pi
    vcftools --gzvcf "$REC_VCFGZ" \
        --window-pi 10000 \
        --out "$OUTPUT_PI"

    # Step 4: Clean up compressed VCF
    rm "$REC_VCFGZ"

    echo "Finished processing $POP"
    echo "---------------------------"
done

Make executable

chmod +x run_pi_calculations.sh

Run in tmux

# Run in tmux
tmux new -s pi_calc
# Reattach later
tmux attach-session -t pi_calc

Load dataframe

pi.all.dataframe<-read.table("/home/Shared_Data/ROD_CADO/analysis/raw.vcf/ROD.CADO.all.pi.windowed.pi", sep="\t", header=T)
pi.concon.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.con-con.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.conrod.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.con-rod.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.strcon.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.str-con.pi.windowed.pi.windowed.pi", sep="\t", header=T)
pi.strrod.dataframe<-read.table("/home/jgreen/ROD_CADO_working/Nucleotide_diversity/ROD.CADO.str-rod.pi.windowed.pi.windowed.pi", sep="\t", header=T)

Color palette

#Here is the color pallette that we will use for everything:

col_pal <- c("#0072B2", "#56B4E9", "#E69F00", "#F0E442")

#Let's factor treatments as follows:

df$TREAT <- factor(df$TREAT, levels=c("CONCON", "STRCON", "CONROD", "STRROD"))

Modify CHROM column in dataframe

pi.all.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.all.df
pi.all.df$CHROM <- as.factor(pi.all.df$CHROM)

pi.concon.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.concon.df
pi.concon.df$CHROM <- as.factor(pi.concon.df$CHROM)

pi.conrod.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.conrod.df
pi.conrod.df$CHROM <- as.factor(pi.conrod.df$CHROM)

pi.strcon.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.strcon.df
pi.strcon.df$CHROM <- as.factor(pi.strcon.df$CHROM)

pi.strrod.dataframe %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035780.1", "1")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035781.1", "2")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035782.1", "3")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035783.1", "4")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035784.1", "5")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035785.1", "6")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035786.1", "7")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035787.1", "8")) %>%
  mutate(CHROM = str_replace(CHROM, "NC_035788.1", "9")) %>% 
  mutate(CHROM = str_replace(CHROM, "NC_035789.1", "10"))  -> pi.strrod.df
pi.strrod.df$CHROM <- as.factor(pi.strrod.df$CHROM)

For loop to replace previous dataframe manipulation

# Create named vector to map chromosome names
chrom_map <- setNames(as.character(1:10), paste0("NC_03578", 0:9, ".1"))

# List of original dataframe names (as strings)
input_names <- c(
  "pi.all.dataframe",
  "pi.concon.dataframe",
  "pi.conrod.dataframe",
  "pi.strcon.dataframe",
  "pi.strrod.dataframe"
)

# Corresponding output dataframe names
output_names <- c(
  "pi.all.df",
  "pi.concon.df",
  "pi.conrod.df",
  "pi.strcon.df",
  "pi.strrod.df"
)

# Loop through each dataframe
for (i in seq_along(input_names)) {
  df <- get(input_names[i])  # retrieve the dataframe by name
  
  # Replace chromosome names
  for (old in names(chrom_map)) {
    df <- df %>% mutate(CHROM = str_replace(CHROM, old, chrom_map[[old]]))
  }
  
  # Convert to factor
  df$CHROM <- as.factor(df$CHROM)
  
  # Assign to new name in global environment
  assign(output_names[i], df)
}

Descriptive statistics

summary(pi.all.df)

     CHROM        BIN_START            BIN_END            N_VARIANTS           PI           
 5      :7644   Min.   :        1   Min.   :    10000   Min.   :   1.0   Min.   :2.480e-06  
 3      :5256   1st Qu.: 18235001   1st Qu.: 18245000   1st Qu.:  79.0   1st Qu.:1.587e-03  
 1      :4775   Median : 36560001   Median : 36570000   Median : 390.0   Median :8.018e-03  
 2      :4506   Mean   : 38071477   Mean   : 38081476   Mean   : 379.1   Mean   :7.928e-03  
 4      :4503   3rd Qu.: 54970001   3rd Qu.: 54980000   3rd Qu.: 601.0   3rd Qu.:1.265e-02  
 9      :4402   Max.   :104140001   Max.   :104150000   Max.   :1405.0   Max.   :3.170e-02  
 (Other):9893                                                                               

by(pi.all.df, pi.all.df$CHROM, summary)

pi.all.df$CHROM: 1
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 1      :4775   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :2.484e-06  
 10     :   0   1st Qu.:17395001   1st Qu.:17405000   1st Qu.: 159.0   1st Qu.:3.297e-03  
 2      :   0   Median :32860001   Median :32870000   Median : 432.0   Median :8.840e-03  
 3      :   0   Mean   :32981320   Mean   :32991319   Mean   : 411.4   Mean   :8.537e-03  
 4      :   0   3rd Qu.:49655001   3rd Qu.:49665000   3rd Qu.: 616.0   3rd Qu.:1.284e-02  
 5      :   0   Max.   :65650001   Max.   :65660000   Max.   :1318.0   Max.   :2.874e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 10
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 10     :1182   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :2.480e-06  
 1      :   0   1st Qu.: 5392501   1st Qu.: 5402500   1st Qu.:   4.0   1st Qu.:7.834e-05  
 2      :   0   Median :11405001   Median :11415000   Median :  54.0   Median :1.049e-03  
 3      :   0   Mean   :14438910   Mean   :14448909   Mean   : 238.2   Mean   :5.024e-03  
 4      :   0   3rd Qu.:23822501   3rd Qu.:23832500   3rd Qu.: 435.8   3rd Qu.:9.352e-03  
 5      :   0   Max.   :32640001   Max.   :32650000   Max.   :1217.0   Max.   :3.170e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 2
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 2      :4506   Min.   :  120001   Min.   :  130000   Min.   :   1.0   Min.   :2.484e-06  
 1      :   0   1st Qu.:12982501   1st Qu.:12992500   1st Qu.: 258.0   1st Qu.:5.168e-03  
 10     :   0   Median :27065001   Median :27075000   Median : 480.0   Median :9.915e-03  
 3      :   0   Mean   :28315551   Mean   :28325550   Mean   : 451.2   Mean   :9.495e-03  
 4      :   0   3rd Qu.:42497501   3rd Qu.:42507500   3rd Qu.: 641.8   3rd Qu.:1.364e-02  
 5      :   0   Max.   :61750001   Max.   :61760000   Max.   :1255.0   Max.   :2.637e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 3
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 3      :5256   Min.   :  170001   Min.   :  180000   Min.   :   1.0   Min.   :2.484e-06  
 1      :   0   1st Qu.:24367501   1st Qu.:24377500   1st Qu.: 160.0   1st Qu.:3.282e-03  
 10     :   0   Median :42575001   Median :42585000   Median : 425.5   Median :8.819e-03  
 2      :   0   Mean   :41219491   Mean   :41229490   Mean   : 406.1   Mean   :8.469e-03  
 4      :   0   3rd Qu.:58222501   3rd Qu.:58232500   3rd Qu.: 605.0   3rd Qu.:1.265e-02  
 5      :   0   Max.   :77050001   Max.   :77060000   Max.   :1243.0   Max.   :3.054e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 4
     CHROM        BIN_START           BIN_END           N_VARIANTS         PI           
 4      :4503   Min.   : 1110001   Min.   : 1120000   Min.   :   1   Min.   :2.484e-06  
 1      :   0   1st Qu.:15335001   1st Qu.:15345000   1st Qu.: 251   1st Qu.:4.919e-03  
 10     :   0   Median :29110001   Median :29120000   Median : 449   Median :9.126e-03  
 2      :   0   Mean   :29882215   Mean   :29892214   Mean   : 430   Mean   :8.924e-03  
 3      :   0   3rd Qu.:45285001   3rd Qu.:45295000   3rd Qu.: 605   3rd Qu.:1.268e-02  
 5      :   0   Max.   :58750001   Max.   :58760000   Max.   :1317   Max.   :2.658e-02  
 (Other):   0                                                                           
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 5
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 5      :7644   Min.   :  660001   Min.   :  670000   Min.   :   1.0   Min.   :2.484e-06  
 1      :   0   1st Qu.:25677501   1st Qu.:25687500   1st Qu.: 192.0   1st Qu.:3.927e-03  
 10     :   0   Median :49065001   Median :49075000   Median : 442.0   Median :9.203e-03  
 2      :   0   Mean   :48514738   Mean   :48524737   Mean   : 422.8   Mean   :8.913e-03  
 3      :   0   3rd Qu.:70842501   3rd Qu.:70852500   3rd Qu.: 620.0   3rd Qu.:1.317e-02  
 4      :   0   Max.   :98660001   Max.   :98670000   Max.   :1363.0   Max.   :2.826e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 6
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 6      :2244   Min.   :  150001   Min.   :  160000   Min.   :   1.0   Min.   :2.484e-06  
 1      :   0   1st Qu.:14317501   1st Qu.:14327500   1st Qu.:  11.0   1st Qu.:2.004e-04  
 10     :   0   Median :30895001   Median :30905000   Median : 188.5   Median :4.145e-03  
 2      :   0   Mean   :26803045   Mean   :26813044   Mean   : 277.0   Mean   :6.240e-03  
 3      :   0   3rd Qu.:37502501   3rd Qu.:37512500   3rd Qu.: 502.2   3rd Qu.:1.155e-02  
 4      :   0   Max.   :51240001   Max.   :51250000   Max.   :1201.0   Max.   :2.765e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 7
     CHROM        BIN_START           BIN_END           N_VARIANTS         PI           
 7      :2721   Min.   :  190001   Min.   :  200000   Min.   :   1   Min.   :2.484e-06  
 1      :   0   1st Qu.:15630001   1st Qu.:15640000   1st Qu.:  13   1st Qu.:2.532e-04  
 10     :   0   Median :33750001   Median :33760000   Median : 168   Median :3.510e-03  
 2      :   0   Mean   :31211409   Mean   :31221408   Mean   : 306   Mean   :6.322e-03  
 3      :   0   3rd Qu.:46220001   3rd Qu.:46230000   3rd Qu.: 585   3rd Qu.:1.192e-02  
 4      :   0   Max.   :57830001   Max.   :57840000   Max.   :1349   Max.   :2.803e-02  
 (Other):   0                                                                           
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 8
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 8      :3746   Min.   :   20001   Min.   :   30000   Min.   :   1.0   Min.   :2.484e-06  
 1      :   0   1st Qu.:22132501   1st Qu.:22142500   1st Qu.:  23.0   1st Qu.:4.535e-04  
 10     :   0   Median :44100001   Median :44110000   Median : 236.0   Median :4.775e-03  
 2      :   0   Mean   :39928872   Mean   :39938871   Mean   : 334.1   Mean   :6.939e-03  
 3      :   0   3rd Qu.:57717501   3rd Qu.:57727500   3rd Qu.: 606.0   3rd Qu.:1.254e-02  
 4      :   0   Max.   :75940001   Max.   :75950000   Max.   :1405.0   Max.   :2.913e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.all.df$CHROM: 9
     CHROM        BIN_START            BIN_END            N_VARIANTS           PI           
 9      :4402   Min.   :    10001   Min.   :    20000   Min.   :   1.0   Min.   :2.484e-06  
 1      :   0   1st Qu.: 30372501   1st Qu.: 30382500   1st Qu.:  10.0   1st Qu.:1.935e-04  
 10     :   0   Median : 60985001   Median : 60995000   Median : 151.0   Median :2.901e-03  
 2      :   0   Mean   : 54813006   Mean   : 54823005   Mean   : 283.8   Mean   :5.765e-03  
 3      :   0   3rd Qu.: 76877501   3rd Qu.: 76887500   3rd Qu.: 527.0   3rd Qu.:1.080e-02  
 4      :   0   Max.   :104140001   Max.   :104150000   Max.   :1372.0   Max.   :2.812e-02  
 (Other):   0                                                                               

cor(pi.all.df$N_VARIANTS, pi.all.df$PI)

[1] 0.9741407

summary(pi.concon.df)

     CHROM        BIN_START            BIN_END            N_VARIANTS           PI          
 5      :7631   Min.   :        1   Min.   :    10000   Min.   :   1.0   Min.   :0.000005  
 3      :5247   1st Qu.: 18245001   1st Qu.: 18255000   1st Qu.:  70.0   1st Qu.:0.001647  
 1      :4769   Median : 36570001   Median : 36580000   Median : 337.0   Median :0.008080  
 2      :4497   Mean   : 38068576   Mean   : 38078575   Mean   : 327.2   Mean   :0.008000  
 4      :4497   3rd Qu.: 54965001   3rd Qu.: 54975000   3rd Qu.: 518.0   3rd Qu.:0.012740  
 9      :4362   Max.   :104140001   Max.   :104150000   Max.   :1231.0   Max.   :0.030658  
 (Other):9824                                                                              

by(pi.concon.df, pi.concon.df$CHROM, summary)

pi.concon.df$CHROM: 1
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 1      :4769   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :0.000005  
 10     :   0   1st Qu.:17390001   1st Qu.:17400000   1st Qu.: 138.0   1st Qu.:0.003378  
 2      :   0   Median :32870001   Median :32880000   Median : 369.0   Median :0.008848  
 3      :   0   Mean   :32985052   Mean   :32995051   Mean   : 353.2   Mean   :0.008564  
 4      :   0   3rd Qu.:49650001   3rd Qu.:49660000   3rd Qu.: 529.0   3rd Qu.:0.012829  
 5      :   0   Max.   :65650001   Max.   :65660000   Max.   :1098.0   Max.   :0.029034  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 10
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 10     :1169   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :5.000e-06  
 1      :   0   1st Qu.: 5380001   1st Qu.: 5390000   1st Qu.:   4.0   1st Qu.:8.679e-05  
 2      :   0   Median :11360001   Median :11370000   Median :  48.0   Median :1.067e-03  
 3      :   0   Mean   :14375852   Mean   :14385851   Mean   : 205.9   Mean   :5.081e-03  
 4      :   0   3rd Qu.:23610001   3rd Qu.:23620000   3rd Qu.: 376.0   3rd Qu.:9.252e-03  
 5      :   0   Max.   :32640001   Max.   :32650000   Max.   :1046.0   Max.   :2.980e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 2
     CHROM        BIN_START           BIN_END           N_VARIANTS         PI          
 2      :4497   Min.   :  120001   Min.   :  130000   Min.   :   1   Min.   :0.000005  
 1      :   0   1st Qu.:12970001   1st Qu.:12980000   1st Qu.: 220   1st Qu.:0.005125  
 10     :   0   Median :27030001   Median :27040000   Median : 405   Median :0.009877  
 3      :   0   Mean   :28285614   Mean   :28295613   Mean   : 384   Mean   :0.009485  
 4      :   0   3rd Qu.:42380001   3rd Qu.:42390000   3rd Qu.: 547   3rd Qu.:0.013546  
 5      :   0   Max.   :61750001   Max.   :61760000   Max.   :1089   Max.   :0.026280  
 (Other):   0                                                                          
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 3
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 3      :5247   Min.   :  170001   Min.   :  180000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:24395001   1st Qu.:24405000   1st Qu.: 141.0   1st Qu.:0.003375  
 10     :   0   Median :42590001   Median :42600000   Median : 372.0   Median :0.008868  
 2      :   0   Mean   :41239465   Mean   :41249464   Mean   : 354.2   Mean   :0.008546  
 4      :   0   3rd Qu.:58225001   3rd Qu.:58235000   3rd Qu.: 526.0   3rd Qu.:0.012837  
 5      :   0   Max.   :77050001   Max.   :77060000   Max.   :1084.0   Max.   :0.030658  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 4
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 4      :4497   Min.   : 1110001   Min.   : 1120000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:15300001   1st Qu.:15310000   1st Qu.: 215.0   1st Qu.:0.005021  
 10     :   0   Median :29100001   Median :29110000   Median : 387.0   Median :0.009218  
 2      :   0   Mean   :29872512   Mean   :29882511   Mean   : 369.8   Mean   :0.009046  
 3      :   0   3rd Qu.:45280001   3rd Qu.:45290000   3rd Qu.: 521.0   3rd Qu.:0.012866  
 5      :   0   Max.   :58750001   Max.   :58760000   Max.   :1090.0   Max.   :0.026883  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 5
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 5      :7631   Min.   :  660001   Min.   :  670000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:25675001   1st Qu.:25685000   1st Qu.: 165.0   1st Qu.:0.003988  
 10     :   0   Median :49040001   Median :49050000   Median : 381.0   Median :0.009261  
 2      :   0   Mean   :48495906   Mean   :48505905   Mean   : 364.2   Mean   :0.008996  
 3      :   0   3rd Qu.:70760001   3rd Qu.:70770000   3rd Qu.: 532.0   3rd Qu.:0.013272  
 4      :   0   Max.   :98660001   Max.   :98670000   Max.   :1108.0   Max.   :0.028540  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 6
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 6      :2231   Min.   :  150001   Min.   :  160000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:14335001   1st Qu.:14345000   1st Qu.:  10.0   1st Qu.:0.0002154  
 10     :   0   Median :30970001   Median :30980000   Median : 167.0   Median :0.0043364  
 2      :   0   Mean   :26840857   Mean   :26850856   Mean   : 243.4   Mean   :0.0062448  
 3      :   0   3rd Qu.:37525001   3rd Qu.:37535000   3rd Qu.: 443.0   3rd Qu.:0.0115789  
 4      :   0   Max.   :51240001   Max.   :51250000   Max.   :1040.0   Max.   :0.0271813  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 7
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 7      :2697   Min.   :  190001   Min.   :  200000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:15660001   1st Qu.:15670000   1st Qu.:  12.0   1st Qu.:0.000271  
 10     :   0   Median :33800001   Median :33810000   Median : 150.0   Median :0.003542  
 2      :   0   Mean   :31268774   Mean   :31278773   Mean   : 265.6   Mean   :0.006336  
 3      :   0   3rd Qu.:46250001   3rd Qu.:46260000   3rd Qu.: 506.0   3rd Qu.:0.011867  
 4      :   0   Max.   :57830001   Max.   :57840000   Max.   :1220.0   Max.   :0.028580  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 8
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 8      :3727   Min.   :   20001   Min.   :   30000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:22115001   1st Qu.:22125000   1st Qu.:  21.0   1st Qu.:0.0004772  
 10     :   0   Median :44120001   Median :44130000   Median : 205.0   Median :0.0048753  
 2      :   0   Mean   :39915142   Mean   :39925141   Mean   : 289.5   Mean   :0.0070622  
 3      :   0   3rd Qu.:57725001   3rd Qu.:57735000   3rd Qu.: 524.0   3rd Qu.:0.0127248  
 4      :   0   Max.   :75930001   Max.   :75940000   Max.   :1204.0   Max.   :0.0291995  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.concon.df$CHROM: 9
     CHROM        BIN_START            BIN_END            N_VARIANTS           PI           
 9      :4362   Min.   :    10001   Min.   :    20000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.: 30392501   1st Qu.: 30402500   1st Qu.:   9.0   1st Qu.:0.0002049  
 10     :   0   Median : 61025001   Median : 61035000   Median : 132.5   Median :0.0029893  
 2      :   0   Mean   : 54824455   Mean   : 54834454   Mean   : 245.0   Mean   :0.0058796  
 3      :   0   3rd Qu.: 76847501   3rd Qu.: 76857500   3rd Qu.: 453.0   3rd Qu.:0.0110273  
 4      :   0   Max.   :104140001   Max.   :104150000   Max.   :1231.0   Max.   :0.0284097  
 (Other):   0                                                                               

cor(pi.concon.df$N_VARIANTS, pi.concon.df$PI)

[1] 0.9810872

summary(pi.conrod.df)

     CHROM        BIN_START            BIN_END            N_VARIANTS           PI          
 5      :7634   Min.   :        1   Min.   :    10000   Min.   :   1.0   Min.   :0.000005  
 3      :5251   1st Qu.: 18240001   1st Qu.: 18250000   1st Qu.:  72.0   1st Qu.:0.001714  
 1      :4768   Median : 36570001   Median : 36580000   Median : 344.0   Median :0.008580  
 2      :4502   Mean   : 38078649   Mean   : 38088648   Mean   : 333.2   Mean   :0.008443  
 4      :4497   3rd Qu.: 54980001   3rd Qu.: 54990000   3rd Qu.: 528.0   3rd Qu.:0.013444  
 9      :4360   Max.   :104140001   Max.   :104150000   Max.   :1240.0   Max.   :0.033498  
 (Other):9826                                                                              

by(pi.conrod.df, pi.conrod.df$CHROM, summary)

pi.conrod.df$CHROM: 1
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 1      :4768   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :0.000005  
 10     :   0   1st Qu.:17377501   1st Qu.:17387500   1st Qu.: 143.0   1st Qu.:0.003494  
 2      :   0   Median :32855001   Median :32865000   Median : 377.0   Median :0.009522  
 3      :   0   Mean   :32971295   Mean   :32981294   Mean   : 360.1   Mean   :0.009078  
 4      :   0   3rd Qu.:49642501   3rd Qu.:49652500   3rd Qu.: 538.0   3rd Qu.:0.013615  
 5      :   0   Max.   :65650001   Max.   :65660000   Max.   :1139.0   Max.   :0.030267  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 10
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 10     :1169   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.: 5370001   1st Qu.: 5380000   1st Qu.:   4.0   1st Qu.:0.000085  
 2      :   0   Median :11370001   Median :11380000   Median :  51.0   Median :0.001161  
 3      :   0   Mean   :14393611   Mean   :14403610   Mean   : 212.1   Mean   :0.005374  
 4      :   0   3rd Qu.:23840001   3rd Qu.:23850000   3rd Qu.: 389.0   3rd Qu.:0.009952  
 5      :   0   Max.   :32640001   Max.   :32650000   Max.   :1076.0   Max.   :0.033498  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 2
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 2      :4502   Min.   :  120001   Min.   :  130000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:12972501   1st Qu.:12982500   1st Qu.: 226.2   1st Qu.:0.005667  
 10     :   0   Median :27065001   Median :27075000   Median : 422.0   Median :0.010671  
 3      :   0   Mean   :28308853   Mean   :28318852   Mean   : 396.1   Mean   :0.010163  
 4      :   0   3rd Qu.:42487501   3rd Qu.:42497500   3rd Qu.: 565.0   3rd Qu.:0.014577  
 5      :   0   Max.   :61750001   Max.   :61760000   Max.   :1089.0   Max.   :0.027570  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 3
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 3      :5251   Min.   :  170001   Min.   :  180000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:24385001   1st Qu.:24395000   1st Qu.: 142.0   1st Qu.:0.003440  
 10     :   0   Median :42590001   Median :42600000   Median : 377.0   Median :0.009299  
 2      :   0   Mean   :41233235   Mean   :41243234   Mean   : 358.6   Mean   :0.008941  
 4      :   0   3rd Qu.:58225001   3rd Qu.:58235000   3rd Qu.: 535.0   3rd Qu.:0.013371  
 5      :   0   Max.   :77050001   Max.   :77060000   Max.   :1047.0   Max.   :0.031353  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 4
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 4      :4497   Min.   : 1110001   Min.   : 1120000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:15300001   1st Qu.:15310000   1st Qu.: 219.0   1st Qu.:0.005305  
 10     :   0   Median :29080001   Median :29090000   Median : 389.0   Median :0.009781  
 2      :   0   Mean   :29857593   Mean   :29867592   Mean   : 373.9   Mean   :0.009496  
 3      :   0   3rd Qu.:45260001   3rd Qu.:45270000   3rd Qu.: 524.0   3rd Qu.:0.013444  
 5      :   0   Max.   :58750001   Max.   :58760000   Max.   :1120.0   Max.   :0.028189  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 5
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 5      :7634   Min.   :  660001   Min.   :  670000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:25692501   1st Qu.:25702500   1st Qu.: 167.2   1st Qu.:0.004194  
 10     :   0   Median :49065001   Median :49075000   Median : 387.0   Median :0.009881  
 2      :   0   Mean   :48523953   Mean   :48533952   Mean   : 370.6   Mean   :0.009517  
 3      :   0   3rd Qu.:70847501   3rd Qu.:70857500   3rd Qu.: 542.0   3rd Qu.:0.014052  
 4      :   0   Max.   :98660001   Max.   :98670000   Max.   :1191.0   Max.   :0.029972  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 6
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 6      :2227   Min.   :  150001   Min.   :  160000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:14325001   1st Qu.:14335000   1st Qu.:  10.0   1st Qu.:0.0002295  
 10     :   0   Median :30950001   Median :30960000   Median : 180.0   Median :0.0046755  
 2      :   0   Mean   :26823885   Mean   :26833884   Mean   : 252.6   Mean   :0.0067466  
 3      :   0   3rd Qu.:37505001   3rd Qu.:37515000   3rd Qu.: 458.0   3rd Qu.:0.0125443  
 4      :   0   Max.   :51160001   Max.   :51170000   Max.   :1090.0   Max.   :0.0286459  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 7
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 7      :2704   Min.   :  190001   Min.   :  200000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:15647501   1st Qu.:15657500   1st Qu.:  12.0   1st Qu.:0.000270  
 10     :   0   Median :33775001   Median :33785000   Median : 154.0   Median :0.003904  
 2      :   0   Mean   :31243318   Mean   :31253317   Mean   : 272.3   Mean   :0.006807  
 3      :   0   3rd Qu.:46242501   3rd Qu.:46252500   3rd Qu.: 519.2   3rd Qu.:0.012744  
 4      :   0   Max.   :57830001   Max.   :57840000   Max.   :1215.0   Max.   :0.028933  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 8
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 8      :3726   Min.   :   20001   Min.   :   30000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:22122501   1st Qu.:22132500   1st Qu.:  21.0   1st Qu.:0.0004945  
 10     :   0   Median :44115001   Median :44125000   Median : 205.0   Median :0.0050893  
 2      :   0   Mean   :39923834   Mean   :39933833   Mean   : 291.7   Mean   :0.0073059  
 3      :   0   3rd Qu.:57727501   3rd Qu.:57737500   3rd Qu.: 525.0   3rd Qu.:0.0131929  
 4      :   0   Max.   :75930001   Max.   :75940000   Max.   :1240.0   Max.   :0.0300153  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.conrod.df$CHROM: 9
     CHROM        BIN_START            BIN_END            N_VARIANTS           PI           
 9      :4360   Min.   :    10001   Min.   :    20000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.: 30620001   1st Qu.: 30630000   1st Qu.:   9.0   1st Qu.:0.0002091  
 10     :   0   Median : 61205001   Median : 61215000   Median : 135.0   Median :0.0031654  
 2      :   0   Mean   : 54904600   Mean   : 54914599   Mean   : 247.6   Mean   :0.0060833  
 3      :   0   3rd Qu.: 76882501   3rd Qu.: 76892500   3rd Qu.: 460.0   3rd Qu.:0.0113625  
 4      :   0   Max.   :104140001   Max.   :104150000   Max.   :1197.0   Max.   :0.0291809  
 (Other):   0                                                                               

cor(pi.conrod.df$N_VARIANTS, pi.conrod.df$PI)

[1] 0.9816607

summary(pi.strcon.df)

     CHROM        BIN_START            BIN_END            N_VARIANTS           PI          
 5      :7629   Min.   :        1   Min.   :    10000   Min.   :   1.0   Min.   :0.000005  
 3      :5245   1st Qu.: 18220001   1st Qu.: 18230000   1st Qu.:  68.0   1st Qu.:0.001609  
 1      :4766   Median : 36560001   Median : 36570000   Median : 324.0   Median :0.007816  
 2      :4496   Mean   : 38067987   Mean   : 38077986   Mean   : 317.2   Mean   :0.007773  
 4      :4493   3rd Qu.: 54980001   3rd Qu.: 54990000   3rd Qu.: 503.0   3rd Qu.:0.012379  
 9      :4375   Max.   :104140001   Max.   :104150000   Max.   :1203.0   Max.   :0.030383  
 (Other):9837                                                                              

by(pi.strcon.df, pi.strcon.df$CHROM, summary)

pi.strcon.df$CHROM: 1
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 1      :4766   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :0.000005  
 10     :   0   1st Qu.:17392501   1st Qu.:17402500   1st Qu.: 134.0   1st Qu.:0.003263  
 2      :   0   Median :32875001   Median :32885000   Median : 354.5   Median :0.008639  
 3      :   0   Mean   :32994976   Mean   :33004975   Mean   : 340.1   Mean   :0.008386  
 4      :   0   3rd Qu.:49677501   3rd Qu.:49687500   3rd Qu.: 511.8   3rd Qu.:0.012604  
 5      :   0   Max.   :65650001   Max.   :65660000   Max.   :1083.0   Max.   :0.028245  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 10
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 10     :1167   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :5.000e-06  
 1      :   0   1st Qu.: 5375001   1st Qu.: 5385000   1st Qu.:   4.0   1st Qu.:8.699e-05  
 2      :   0   Median :11350001   Median :11360000   Median :  50.0   Median :1.040e-03  
 3      :   0   Mean   :14379418   Mean   :14389417   Mean   : 203.9   Mean   :4.923e-03  
 4      :   0   3rd Qu.:23675001   3rd Qu.:23685000   3rd Qu.: 372.0   3rd Qu.:9.060e-03  
 5      :   0   Max.   :32640001   Max.   :32650000   Max.   :1091.0   Max.   :3.038e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 2
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 2      :4496   Min.   :  120001   Min.   :  130000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:12977501   1st Qu.:12987500   1st Qu.: 215.0   1st Qu.:0.005052  
 10     :   0   Median :27045001   Median :27055000   Median : 395.0   Median :0.009629  
 3      :   0   Mean   :28303061   Mean   :28313060   Mean   : 375.7   Mean   :0.009268  
 4      :   0   3rd Qu.:42482501   3rd Qu.:42492500   3rd Qu.: 535.0   3rd Qu.:0.013337  
 5      :   0   Max.   :61750001   Max.   :61760000   Max.   :1063.0   Max.   :0.026673  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 3
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 3      :5245   Min.   :  170001   Min.   :  180000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:24390001   1st Qu.:24400000   1st Qu.: 138.0   1st Qu.:0.003325  
 10     :   0   Median :42600001   Median :42610000   Median : 358.0   Median :0.008658  
 2      :   0   Mean   :41233854   Mean   :41243853   Mean   : 344.6   Mean   :0.008326  
 4      :   0   3rd Qu.:58230001   3rd Qu.:58240000   3rd Qu.: 512.0   3rd Qu.:0.012417  
 5      :   0   Max.   :77050001   Max.   :77060000   Max.   :1108.0   Max.   :0.029710  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 4
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 4      :4493   Min.   : 1110001   Min.   : 1120000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:15290001   1st Qu.:15300000   1st Qu.: 208.0   1st Qu.:0.004789  
 10     :   0   Median :29080001   Median :29090000   Median : 373.0   Median :0.008899  
 2      :   0   Mean   :29861717   Mean   :29871716   Mean   : 359.6   Mean   :0.008714  
 3      :   0   3rd Qu.:45290001   3rd Qu.:45300000   3rd Qu.: 503.0   3rd Qu.:0.012380  
 5      :   0   Max.   :58750001   Max.   :58760000   Max.   :1021.0   Max.   :0.026611  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 5
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 5      :7629   Min.   :  660001   Min.   :  670000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:25650001   1st Qu.:25660000   1st Qu.: 161.0   1st Qu.:0.003863  
 10     :   0   Median :49010001   Median :49020000   Median : 364.0   Median :0.008906  
 2      :   0   Mean   :48465698   Mean   :48475697   Mean   : 351.7   Mean   :0.008700  
 3      :   0   3rd Qu.:70730001   3rd Qu.:70740000   3rd Qu.: 515.0   3rd Qu.:0.012889  
 4      :   0   Max.   :98660001   Max.   :98670000   Max.   :1133.0   Max.   :0.027765  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 6
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 6      :2233   Min.   :  150001   Min.   :  160000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:14320001   1st Qu.:14330000   1st Qu.:   9.0   1st Qu.:0.0002159  
 10     :   0   Median :30930001   Median :30940000   Median : 156.0   Median :0.0042449  
 2      :   0   Mean   :26804632   Mean   :26814631   Mean   : 230.4   Mean   :0.0061215  
 3      :   0   3rd Qu.:37510001   3rd Qu.:37520000   3rd Qu.: 413.0   3rd Qu.:0.0113437  
 4      :   0   Max.   :51240001   Max.   :51250000   Max.   :1030.0   Max.   :0.0279714  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 7
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 7      :2710   Min.   :  190001   Min.   :  200000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:15632501   1st Qu.:15642500   1st Qu.:  11.0   1st Qu.:0.0002554  
 10     :   0   Median :33755001   Median :33765000   Median : 145.0   Median :0.0034174  
 2      :   0   Mean   :31205141   Mean   :31215140   Mean   : 254.7   Mean   :0.0061798  
 3      :   0   3rd Qu.:46217501   3rd Qu.:46227500   3rd Qu.: 482.0   3rd Qu.:0.0115770  
 4      :   0   Max.   :57830001   Max.   :57840000   Max.   :1134.0   Max.   :0.0272090  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 8
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 8      :3727   Min.   :   20001   Min.   :   30000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:22135001   1st Qu.:22145000   1st Qu.:  20.0   1st Qu.:0.0004778  
 10     :   0   Median :44140001   Median :44150000   Median : 195.0   Median :0.0047001  
 2      :   0   Mean   :39954407   Mean   :39964406   Mean   : 282.7   Mean   :0.0068577  
 3      :   0   3rd Qu.:57755001   3rd Qu.:57765000   3rd Qu.: 510.0   3rd Qu.:0.0123940  
 4      :   0   Max.   :75940001   Max.   :75950000   Max.   :1203.0   Max.   :0.0291044  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.strcon.df$CHROM: 9
     CHROM        BIN_START            BIN_END            N_VARIANTS           PI           
 9      :4375   Min.   :    10001   Min.   :    20000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.: 30365001   1st Qu.: 30375000   1st Qu.:   9.0   1st Qu.:0.0002015  
 10     :   0   Median : 61120001   Median : 61130000   Median : 128.0   Median :0.0028850  
 2      :   0   Mean   : 54841932   Mean   : 54851931   Mean   : 238.5   Mean   :0.0056882  
 3      :   0   3rd Qu.: 76895001   3rd Qu.: 76905000   3rd Qu.: 441.0   3rd Qu.:0.0106289  
 4      :   0   Max.   :104140001   Max.   :104150000   Max.   :1150.0   Max.   :0.0276833  
 (Other):   0                                                                               

cor(pi.strcon.df$N_VARIANTS, pi.strcon.df$PI)

[1] 0.9797857

summary(pi.strrod.df)

     CHROM        BIN_START            BIN_END            N_VARIANTS           PI          
 5      :7631   Min.   :        1   Min.   :    10000   Min.   :   1.0   Min.   :0.000005  
 3      :5249   1st Qu.: 18240001   1st Qu.: 18250000   1st Qu.:  69.0   1st Qu.:0.001581  
 1      :4764   Median : 36580001   Median : 36590000   Median : 328.0   Median :0.007806  
 2      :4495   Mean   : 38079492   Mean   : 38089491   Mean   : 319.4   Mean   :0.007733  
 4      :4495   3rd Qu.: 54990001   3rd Qu.: 55000000   3rd Qu.: 505.0   3rd Qu.:0.012330  
 9      :4378   Max.   :104140001   Max.   :104150000   Max.   :1204.0   Max.   :0.033066  
 (Other):9829                                                                              

by(pi.strrod.df, pi.strrod.df$CHROM, summary)

pi.strrod.df$CHROM: 1
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 1      :4764   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :0.000005  
 10     :   0   1st Qu.:17367501   1st Qu.:17377500   1st Qu.: 137.8   1st Qu.:0.003265  
 2      :   0   Median :32855001   Median :32865000   Median : 366.0   Median :0.008611  
 3      :   0   Mean   :32976376   Mean   :32986375   Mean   : 348.7   Mean   :0.008348  
 4      :   0   3rd Qu.:49662501   3rd Qu.:49672500   3rd Qu.: 523.2   3rd Qu.:0.012520  
 5      :   0   Max.   :65650001   Max.   :65660000   Max.   :1204.0   Max.   :0.028473  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 10
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 10     :1167   Min.   :       1   Min.   :   10000   Min.   :   1.0   Min.   :5.000e-06  
 1      :   0   1st Qu.: 5375001   1st Qu.: 5385000   1st Qu.:   4.0   1st Qu.:8.032e-05  
 2      :   0   Median :11370001   Median :11380000   Median :  49.0   Median :1.076e-03  
 3      :   0   Mean   :14416702   Mean   :14426701   Mean   : 207.6   Mean   :5.007e-03  
 4      :   0   3rd Qu.:23860001   3rd Qu.:23870000   3rd Qu.: 380.0   3rd Qu.:9.256e-03  
 5      :   0   Max.   :32640001   Max.   :32650000   Max.   :1092.0   Max.   :3.307e-02  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 2
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 2      :4495   Min.   :  120001   Min.   :  130000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:12965001   1st Qu.:12975000   1st Qu.: 220.0   1st Qu.:0.005022  
 10     :   0   Median :27050001   Median :27060000   Median : 410.0   Median :0.009756  
 3      :   0   Mean   :28297690   Mean   :28307689   Mean   : 385.5   Mean   :0.009299  
 4      :   0   3rd Qu.:42475001   3rd Qu.:42485000   3rd Qu.: 548.0   3rd Qu.:0.013313  
 5      :   0   Max.   :61750001   Max.   :61760000   Max.   :1016.0   Max.   :0.026039  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 3
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 3      :5249   Min.   :  170001   Min.   :  180000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:24390001   1st Qu.:24400000   1st Qu.: 137.0   1st Qu.:0.003253  
 10     :   0   Median :42590001   Median :42600000   Median : 360.0   Median :0.008483  
 2      :   0   Mean   :41238174   Mean   :41248173   Mean   : 343.6   Mean   :0.008230  
 4      :   0   3rd Qu.:58230001   3rd Qu.:58240000   3rd Qu.: 508.0   3rd Qu.:0.012345  
 5      :   0   Max.   :77050001   Max.   :77060000   Max.   :1095.0   Max.   :0.030999  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 4
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 4      :4495   Min.   : 1110001   Min.   : 1120000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:15295001   1st Qu.:15305000   1st Qu.: 205.0   1st Qu.:0.004657  
 10     :   0   Median :29080001   Median :29090000   Median : 369.0   Median :0.008767  
 2      :   0   Mean   :29858715   Mean   :29868714   Mean   : 355.4   Mean   :0.008627  
 3      :   0   3rd Qu.:45265001   3rd Qu.:45275000   3rd Qu.: 499.0   3rd Qu.:0.012329  
 5      :   0   Max.   :58750001   Max.   :58760000   Max.   :1088.0   Max.   :0.025404  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 5
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI          
 5      :7631   Min.   :  660001   Min.   :  670000   Min.   :   1.0   Min.   :0.000005  
 1      :   0   1st Qu.:25655001   1st Qu.:25665000   1st Qu.: 161.0   1st Qu.:0.003800  
 10     :   0   Median :49040001   Median :49050000   Median : 369.0   Median :0.008900  
 2      :   0   Mean   :48491636   Mean   :48501635   Mean   : 352.3   Mean   :0.008655  
 3      :   0   3rd Qu.:70785001   3rd Qu.:70795000   3rd Qu.: 517.0   3rd Qu.:0.012793  
 4      :   0   Max.   :98660001   Max.   :98670000   Max.   :1113.0   Max.   :0.027615  
 (Other):   0                                                                            
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 6
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 6      :2230   Min.   :  150001   Min.   :  160000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:14322501   1st Qu.:14332500   1st Qu.:  10.0   1st Qu.:0.0002097  
 10     :   0   Median :30955001   Median :30965000   Median : 165.0   Median :0.0041790  
 2      :   0   Mean   :26827521   Mean   :26837520   Mean   : 233.3   Mean   :0.0061332  
 3      :   0   3rd Qu.:37517501   3rd Qu.:37527500   3rd Qu.: 421.8   3rd Qu.:0.0112801  
 4      :   0   Max.   :51240001   Max.   :51250000   Max.   :1029.0   Max.   :0.0270868  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 7
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 7      :2704   Min.   :  190001   Min.   :  200000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:15657501   1st Qu.:15667500   1st Qu.:  12.0   1st Qu.:0.0002639  
 10     :   0   Median :33785001   Median :33795000   Median : 146.0   Median :0.0034714  
 2      :   0   Mean   :31246369   Mean   :31256368   Mean   : 256.8   Mean   :0.0061921  
 3      :   0   3rd Qu.:46232501   3rd Qu.:46242500   3rd Qu.: 485.0   3rd Qu.:0.0115946  
 4      :   0   Max.   :57830001   Max.   :57840000   Max.   :1136.0   Max.   :0.0277927  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 8
     CHROM        BIN_START           BIN_END           N_VARIANTS           PI           
 8      :3728   Min.   :   20001   Min.   :   30000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.:22117501   1st Qu.:22127500   1st Qu.:  20.0   1st Qu.:0.0004723  
 10     :   0   Median :44145001   Median :44155000   Median : 194.5   Median :0.0046746  
 2      :   0   Mean   :39939942   Mean   :39949941   Mean   : 281.0   Mean   :0.0067395  
 3      :   0   3rd Qu.:57712501   3rd Qu.:57722500   3rd Qu.: 503.2   3rd Qu.:0.0121566  
 4      :   0   Max.   :75940001   Max.   :75950000   Max.   :1191.0   Max.   :0.0288804  
 (Other):   0                                                                             
------------------------------------------------------------------------------------------------------ 
pi.strrod.df$CHROM: 9
     CHROM        BIN_START            BIN_END            N_VARIANTS           PI           
 9      :4378   Min.   :    10001   Min.   :    20000   Min.   :   1.0   Min.   :0.0000050  
 1      :   0   1st Qu.: 30562501   1st Qu.: 30572500   1st Qu.:   9.0   1st Qu.:0.0002024  
 10     :   0   Median : 61135001   Median : 61145000   Median : 131.5   Median :0.0028874  
 2      :   0   Mean   : 54855472   Mean   : 54865471   Mean   : 241.4   Mean   :0.0056743  
 3      :   0   3rd Qu.: 76877501   3rd Qu.: 76887500   3rd Qu.: 445.0   3rd Qu.:0.0106070  
 4      :   0   Max.   :104140001   Max.   :104150000   Max.   :1165.0   Max.   :0.0281665  
 (Other):   0                                                                               

cor(pi.strrod.df$N_VARIANTS, pi.strrod.df$PI)

[1] 0.9795718

New loop and plotting for statistics

col_pal <- c(
  "ALL" = "gray70",
  "CONCON" = "#0072B2", 
  "STRCON" = "#56B4E9", 
  "CONROD" = "#E69F00", 
  "STRROD" = "#F0E442"
)

df_names <- c("pi.all.df", "pi.concon.df", "pi.strcon.df", "pi.conrod.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "STRCON", "CONROD", "STRROD")
chrom_levels <- as.character(1:10)

summary_list <- list()

for (i in seq_along(df_names)) {
  df <- get(df_names[i])
  treat <- df_labels[i]
  
  df$TREAT <- factor(treat, levels = names(col_pal))
  
  chrom_summary <- df %>%
    group_by(CHROM, TREAT) %>%
    summarise(
      mean_PI = mean(PI, na.rm = TRUE),
      se_PI = sd(PI, na.rm = TRUE) / sqrt(n()),
      mean_N_VARIANTS = mean(N_VARIANTS, na.rm = TRUE),
      se_N_VARIANTS = sd(N_VARIANTS, na.rm = TRUE) / sqrt(n()),
      .groups = "drop"
    ) %>%
    mutate(CHROM = factor(CHROM, levels = chrom_levels))
  
  summary_list[[i]] <- chrom_summary
}

summary_df <- bind_rows(summary_list)

mean_pi_plot <- ggplot(summary_df, aes(x = CHROM, y = mean_PI, fill = TREAT)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  geom_errorbar(aes(ymin = mean_PI - se_PI, ymax = mean_PI + se_PI),
                position = position_dodge(width = 0.8), width = 0.2) +
  scale_fill_manual(values = col_pal, name = "Treatment") +
  labs(title = "Mean PI per Chromosome", x = "Chromosome", y = "Mean PI") +
  theme_minimal()

mean_n_plot <- ggplot(summary_df, aes(x = CHROM, y = mean_N_VARIANTS, fill = TREAT)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  geom_errorbar(aes(ymin = mean_N_VARIANTS - se_N_VARIANTS, ymax = mean_N_VARIANTS + se_N_VARIANTS),
                position = position_dodge(width = 0.8), width = 0.2) +
  scale_fill_manual(values = col_pal, name = "Treatment") +
  labs(title = "Mean number of variants per Chromosome", x = "Chromosome", y = "Mean # variants") +
  theme_minimal()

print(mean_pi_plot)

print(mean_n_plot)

ggsave("mean_pi_plot.png", plot = mean_pi_plot, width = 10, height = 6, dpi = 300)

ggsave("mean_n_variants_plot.png", plot = mean_n_plot, width = 10, height = 6, dpi = 300)

Correlation visualizations

for (i in seq_along(df_names)) {
  df <- get(df_names[i])
  label <- df_labels[i]
  
  p <- ggplot(df, aes(x = N_VARIANTS, y = PI)) +
    geom_point(alpha = 0.4) +
    geom_smooth(method = "lm", se = FALSE, color = "blue") +
    labs(title = paste("Correlation: PI vs # variants —", label),
         x = "N_VARIANTS",
         y = "PI") +
    theme_minimal()
  
  print(p)
}

Table of correlations

cor_table <- tibble(
  dataset = df_labels,
  correlation = map_dbl(df_names, ~ cor(get(.x)$N_VARIANTS, get(.x)$PI, use = "complete.obs"))
)

print(cor_table)

Plot PI by chromosome

ggplot(pi.all.df, aes(x=CHROM, y=PI,))+
  geom_violin(aes(color=CHROM,fill=CHROM))+
  geom_boxplot(aes(fill=CHROM), width=0.1,outlier.shape = 23, outlier.color = "black")+
  stat_summary(fun=mean, geom="point", shape=23, size=2)+
  scale_fill_brewer(palette = "Paired")+
  theme_classic()

Plot PI by chromosome loop

# List of dataframes and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("All", "ConCon", "ConRod", "StrCon", "StrRod")

# Standard chromosome order
chrom_levels <- as.character(1:10)

# Combine all into one dataframe
combined_df <- purrr::map2_dfr(df_names, df_labels, function(df_name, label) {
  df <- get(df_name)
  df %>%
    mutate(
      dataset = label,
      CHROM = factor(CHROM, levels = chrom_levels)
    )
})

# Faceted violin + boxplot
ggplot(combined_df, aes(x = CHROM, y = PI)) +
  geom_violin(aes(color = CHROM, fill = CHROM), trim = FALSE) +
  geom_boxplot(aes(fill = CHROM), width = 0.1, outlier.shape = 23, outlier.color = "black") +
  stat_summary(fun = mean, geom = "point", shape = 23, size = 2) +
  scale_fill_brewer(palette = "Paired") +
  labs(title = "PI Distribution by Chromosome (Faceted by Dataset)",
       x = "Chromosome", y = "PI") +
  facet_wrap(~ dataset, ncol = 2) +
  theme_classic() +
  theme(legend.position = "none")

Smaller visualizations

hist(mydf$PI,br=40)

boxplot(mydf$PI, ylab="Nuc Diversity")

Plot By position

ggplot(pi.all.df, aes(x=BIN_START, y=PI, color=CHROM))+
  geom_point()+
  scale_fill_brewer(palette = "Paired") +
  scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) +
  facet_wrap(~CHROM)+
  theme_classic()

Loop for plot by position

# Define dataframe names and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")

# Get global PI range
all_pi_values <- unlist(lapply(df_names, function(x) get(x)$PI))
global_ymin <- min(all_pi_values, na.rm = TRUE)
global_ymax <- max(all_pi_values, na.rm = TRUE)

# Loop over dataframes
for (i in seq_along(df_names)) {
  df <- get(df_names[i])
  label <- df_labels[i]
  
  # Ensure CHROM is a factor ordered from 1 to 10
  df$CHROM <- factor(df$CHROM, levels = as.character(1:10))
  
  p <- ggplot(df, aes(x = BIN_START, y = PI, color = CHROM)) +
    geom_point() +
    facet_wrap(~CHROM) +
    scale_fill_brewer(palette = "Paired") +
    scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) +  # Human readable x-axis
    ylim(global_ymin, global_ymax) +  # Same y-axis for all plots
    theme_classic() +
    labs(title = paste("PI vs BIN_START -", label),
         x = "BIN_START (millions)",
         y = "PI")
  
  print(p)
  
  ggsave(filename = paste0("PI_vs_BIN_START_", label, ".png"),
         plot = p, width = 10, height = 6, dpi = 300)
}

Facet wrap chromosome showing them across different treatment types

# Define dataframe names and labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")

# Custom color palette
col_pal <- c(
  "ALL" = "gray70",
  "CONCON" = "#0072B2", 
  "STRCON" = "#56B4E9", 
  "CONROD" = "#E69F00", 
  "STRROD" = "#F0E442"
)

# Combine all data into one dataframe with treatment labels
all_data <- bind_rows(lapply(seq_along(df_names), function(i) {
  df <- get(df_names[i])
  df$Treatment <- df_labels[i]
  df
}))

# Set CHROM and Treatment as ordered factors
all_data$CHROM <- factor(all_data$CHROM, levels = as.character(1:10))
all_data$Treatment <- factor(all_data$Treatment, levels = df_labels)

# Get global PI range
global_ymin <- min(all_data$PI, na.rm = TRUE)
global_ymax <- max(all_data$PI, na.rm = TRUE)

# Loop through chromosomes 1 to 10
for (chr in 1:10) {
  chr_str <- as.character(chr)
  chr_data <- filter(all_data, CHROM == chr_str)
  
  p <- ggplot(chr_data, aes(x = BIN_START, y = PI, color = Treatment)) +
    geom_point(alpha = 0.6, size = 0.5) +
    facet_wrap(~Treatment, nrow = 1) +
    scale_color_manual(values = col_pal) +
    scale_x_continuous(labels = label_number(scale = 1e-6, suffix = "M")) +
    ylim(global_ymin, global_ymax) +
    theme_classic() +
    labs(
      title = paste("Chromosome", chr, "- PI across Treatment Types"),
      x = "BIN_START (millions)",
      y = "PI"
    )
  
  print(p)
  
  ggsave(
    filename = paste0("PI_chr", chr, "_across_treatments.png"),
    plot = p,
    width = 16,
    height = 4,
    dpi = 300
  )
}

Only chromosome 1

# Subset by chrom
mydf.chr1 <- mydf[which(mydf$CHROM=="1"),]

ggplot(mydf.chr1, aes(x=BIN_START, y=PI))+
  geom_point()+
  theme_classic()

# List of treatment data frames and their labels
df_names <- c("pi.all.df", "pi.concon.df", "pi.conrod.df", "pi.strcon.df", "pi.strrod.df")
df_labels <- c("ALL", "CONCON", "CONROD", "STRCON", "STRROD")

# Step 1: Calculate global PI range across all dataframes
all_pi_values <- unlist(lapply(df_names, function(x) get(x)$PI))
global_ymin <- min(all_pi_values, na.rm = TRUE)
global_ymax <- max(all_pi_values, na.rm = TRUE)

# Step 2: Create plots and save as PNGs
for (j in seq_along(df_names)) {
  df <- get(df_names[j])
  label <- df_labels[j]
  
  for (i in 1:10) {
    chr_data <- df[df$CHROM == as.character(i), ]
    
    p <- ggplot(chr_data, aes(x = BIN_START, y = PI)) +
      geom_point() +
      theme_classic() +
      ggtitle(paste("Treatment:", label, "- Chromosome", i)) +
      labs(x = "BIN_START", y = "PI") +
      ylim(global_ymin, global_ymax)
    
    filename <- paste0("PI_", label, "_chr", i, ".png")
    ggsave(filename = filename, plot = p, width = 8, height = 5, dpi = 300)
  }
}

Runs of homozygosity

Runs of homozygosity (ROH) are contiguous lengths of homozygous genotypes that are present in an individual due to parents transmitting identical haplotypes to their offspring.

The potential of predicting or estimating individual autozygosity for a subpopulation is the proportion of the autosomal genome above a specified length, termed Froh.

This technique can be used to identify the genomic footprint of inbreeding in conservation programs, as organisms that have undergone recent inbreeding will exhibit long runs of homozygosity. The effect of inbreeding in the resulting sub-populations could be studied by measuring the runs of homozygosity in different individuals.

Start ROH workflow

vcftools --vcf SNP.TRSdp10g1.FIL.vcf --LROH --out ROD.CADO.all.LROH